In this paper, we examine dynamic properties of particle flows for a recently derived parameterized family of stochastic particle flow filters for nonlinear filtering and Bayesian inference.
We prove that the particle flows are unbiased under the assumption of linear measurement and Gaussian distributions, and that estimates constructed from the stochastic flows are consistent.
We further demonstrate by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over a single layer.
Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems.
In this paper, we propose to use a Conditional Generative Adversarial Network (CGAN) for distilling (i. e. transferring) knowledge from sensor data and enhancing low-resolution target detection.
A consensus structured analysis dictionary and a global classifier are jointly learned in the distributed approach to safeguard the discriminative power and the efficiency of classification.
We propose a computationally efficient and high-performance classification algorithm by incorporating class structural information in analysis dictionary learning.
Parametric approaches to Learning, such as deep learning (DL), are highly popular in nonlinear regression, in spite of their extremely difficult training with their increasing complexity (e. g. number of layers in DL).
Deep dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics.